A quantitative analysis of the impact of integrating digital technology for formwork fabrication on human factors perspectives

With the current advancements in artificially intelligent machines and robotic systems, the use of digital fabrication tools has become the mainstream of construction industrialization. Digital fabrication facilitates the transformation from design to physical products. Despite the fact that there are several benefits of utilizing digital fabrication in the construction industry, only a few publications have concentrated on how digital technology will increase project productivity, safety, and quality. In detail, while digital fabrication tools hold promise in reducing worker physical activity, there is a lack of knowledge in the psychophysiological aspects of interaction with this data-driven equipment. This knowledge gap can particularly be realized in the sphere of Computer Numerical Control (CNC) systems, where the worker's mental demand is still unknown. This matter's prominence lies in the potent role of high cognitive workload on human errors, one of the leading causes of accidents at construction job sites. To bridge this gap, this study aims to investigate three main objectives: (1) to develop and test workflows from design to fabrication processes for formwork production using digital models and CNC machines. These workflows combine VDC trade coordination, 3D parametric modeling, tool path development, and CNC routing in order to produce precision prefabricated formwork components. The productivity of the developed workflows was measured for evaluation purposes. To validate the examined productivity, a case study including prefabricating concrete edge forms for a 25 story post-tensioned, cast-in-place structure has been shown; (2) to quantify workers' mental workload when using CNC machine and to compare this workload with the case when workers fabricate formwork manually; (3) to quantify workers' emotion when interacting with CNC machine and compare this amount with the case when workers fabricate formwork manually. The second and third objectives were obtained by interpreting participants’ brainwaves recorded from a wearable electroencephalogram (EEG). To that end, ten subjects having the experience of working with both CNC machines and manual tools were recruited to perform formwork fabrication under two different conditions, operating with and without a CNC machine. Various signal processing techniques (e.g., removing movement artifacts, removing environmental noise etc. ) were used to remove EEG artifacts. Then, time and frequency domain analyses were performed to extract different EEG metrics representing workers' cognitive load, such as alpha, beta, and theta power bands. The research team was able to display the difference in mental workload/emotion in the two scenarios using hypothesis testing and different machine learning classifier algorithms. It is expected that the results of this study will provide useful guidelines for contractors to notice the advantages of using CNC machines over that traditional approach and to help them to make better decisions in terms of enhancing their profit margin despite the rising construction-related cost trend.